Balancing uncertainty and complexity to incorporate fire spread in an eco-hydrological model
Maureen C. Kennedy A B E , Donald McKenzie C , Christina Tague D and Aubrey L. Dugger DA University of Washington, School of Environmental and Forest Sciences, Box 352100 Seattle, WA 98195-2100, USA.
B Present address: University of Washington, School of Interdisciplinary Arts and Sciences, 1900 Commerce Street, Box 358436, Tacoma, WA 98402, USA.
C Pacific Wildland Fire Sciences Laboratory, Pacific Northwest Research Station, US Forest Service, 400 N 34th Street, Suite 201, Seattle, WA, USA.
D University of California, Santa Barbara, Bren School of Environmental Science and Management, 2400 University of California, Santa Barbara, CA 93117, USA.
E Corresponding author. Email: mkenn@uw.edu
International Journal of Wildland Fire 26(8) 706-718 https://doi.org/10.1071/WF16169
Submitted: 9 September 2016 Accepted: 12 May 2017 Published: 30 June 2017
Abstract
Wildfire affects the ecosystem services of watersheds, and climate change will modify fire regimes and watershed dynamics. In many eco-hydrological simulations, fire is included as an exogenous force. Rarely are the bidirectional feedbacks between watersheds and fire regimes integrated in a simulation system because the eco-hydrological model predicts variables that are incompatible with the requirements of fire models. WMFire is a fire-spread model of intermediate complexity designed to be integrated with the Regional Hydro-ecological Simulation System (RHESSys). Spread in WMFire is based on four variables that (i) represent known influences on fire spread: litter load, relative moisture deficit, wind direction and topographic slope, and (ii) are derived directly from RHESSys outputs. The probability that a fire spreads from pixel to pixel depends on these variables as predicted by RHESSys. We tested a partial integration between WMFire and RHESSys on the Santa Fe (New Mexico) and the HJ Andrews (Oregon State) watersheds. Model assessment showed correspondence between expected spatial patterns of spread and seasonality in both watersheds. These results demonstrate the efficacy of an approach to link eco-hydrologic model outputs with a fire spread model. Future work will develop a fire effects module in RHESSys for a fully coupled, bidirectional model.
Additional keywords: HJ Andrews, New Mexico, Oregon, Santa Fe watershed.
References
Bartlein P, Hostetler S, Shafer S, Holman J, Soloman A (2008) Temporal and spatial structure in a daily wildfire-start data set from the western United States (1986–96). International Journal of Wildland Fire 17, 8–17.| Temporal and spatial structure in a daily wildfire-start data set from the western United States (1986–96).Crossref | GoogleScholarGoogle Scholar |
Caldarelli G, Frondoni R, Gabrielli A (2001) Percolation in real wildfires. Europhysics Letters 56, 510–516.
| Percolation in real wildfires.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD3MXptVKgu7s%3D&md5=458893c5efc9f47e864050747358b25cCAS |
Environmental Protection Agency (2009) Guidance on the development, evaluation, and application of environmental models. EPA/100/K-09/003|March2009. Available at https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P1003E4R.PDF [Verified 24 May 2017]
Faivre NR, Jin Y, Goulden ML, Randerson JT (2016) Spatial patterns and controls on burned area for two contrasting fire regimes in southern California. Ecosphere 7, e01210
| Spatial patterns and controls on burned area for two contrasting fire regimes in southern California.Crossref | GoogleScholarGoogle Scholar |
Fatichi S, Pappas C, Ivanov VY (2016) Modeling plant–water interactions: an ecohydrological overview from the cell to the global scale. Wiley Interdisciplinary Reviews: Water 3, 327–368.
| Modeling plant–water interactions: an ecohydrological overview from the cell to the global scale.Crossref | GoogleScholarGoogle Scholar |
Finney MA (2004) FARSITE: Fire Area Simulator – Model development and evaluation. USDA Forest Service, Rocky Mountain Research Station, Research Paper RMRS-RP-4 Revised. (Fort Collins, CO, USA)
Fiorucci P, Gaetani F, Minciardi R (2008) Regional partitioning for wildfire regime characterization. Journal of Geophysical Research 113, F02013
| Regional partitioning for wildfire regime characterization.Crossref | GoogleScholarGoogle Scholar |
Flannigan MD, Krawchuk MA, de Groot WJ, Wotton MB, Gowman LM (2009) Implications of changing climate for global wildland fire. International Journal of Wildland Fire 18, 483–507.
| Implications of changing climate for global wildland fire.Crossref | GoogleScholarGoogle Scholar |
Garcia ES, Tague CL (2015) Subsurface storage capacity influences climate – evapotranspiration interactions in three western United States catchments. Hydrology and Earth System Sciences 19, 4845–4858.
| Subsurface storage capacity influences climate – evapotranspiration interactions in three western United States catchments.Crossref | GoogleScholarGoogle Scholar |
Garcia ES, Tague CL, Choate JS (2013) Influence of spatial temperature estimation method in ecohydrologic modeling in the Western Oregon Cascades. Water Resources Research 49, 1611–1624.
| Influence of spatial temperature estimation method in ecohydrologic modeling in the Western Oregon Cascades.Crossref | GoogleScholarGoogle Scholar |
Hannah DM, Wood PJ, Sadler JP (2004) Ecohydrology and hydroecology: a ‘new paradigm’? Hydrological Processes 18, 3439–3445.
| Ecohydrology and hydroecology: a ‘new paradigm’?Crossref | GoogleScholarGoogle Scholar |
Hannah DM, Sadler JP, Wood PJ (2007) Hydroecology and ecohydrology: challenges and future prospects. In ‘Hydroecology and Ecohydrology: Past, Present and Future’. (Eds P Wood, DM Hannah, JP Sadler) pp. 421–429. (Wiley: Chichester, UK)
Heinselman ML (1973) Fire in the virgin forests of the Boundary Waters Canoe Area, Minnesota. Quaternary Research 3, 329–382.
| Fire in the virgin forests of the Boundary Waters Canoe Area, Minnesota.Crossref | GoogleScholarGoogle Scholar |
Hornberger G, Cosby B (1985) Selection of parameter values in environmental models using sparse data: a case study. Applied Mathematics and Computation 17, 335–355.
| Selection of parameter values in environmental models using sparse data: a case study.Crossref | GoogleScholarGoogle Scholar |
Hurteau MD, Bradford JB, Fulé PZ, Taylor AH, Martin KL (2014) Climate change, fire management, and ecological services in the south-western US. Forest Ecology and Management 327, 280–289.
| Climate change, fire management, and ecological services in the south-western US.Crossref | GoogleScholarGoogle Scholar |
Hyde K, Dickinson MB, Bohrer G, Calkin D, Evers L, Gilbertson-Day J, Nicolet T, Ryan K, Tague C (2013) Research and development supporting risk-based wildfire effects prediction for fuels and fire management: status and needs. International Journal of Wildland Fire 22, 37–50.
| Research and development supporting risk-based wildfire effects prediction for fuels and fire management: status and needs.Crossref | GoogleScholarGoogle Scholar |
Jackson LJ, Trebitz AS, Cottingham KL (2000) An introduction to the practice of ecological modeling. Bioscience 50, 694–706.
| An introduction to the practice of ecological modeling.Crossref | GoogleScholarGoogle Scholar |
Keane RE, Parsons RA, Hessburg PF (2002) Estimating historical range and variation of landscape patch dynamics: limitations of the simulation approach. Ecological Modelling 151, 29–49.
| Estimating historical range and variation of landscape patch dynamics: limitations of the simulation approach.Crossref | GoogleScholarGoogle Scholar |
Kennedy MC, McKenzie D (2010) Using a stochastic model and cross-scale analysis to evaluate controls on historical low-severity fire regimes. Landscape Ecology 25, 1561–1573.
| Using a stochastic model and cross-scale analysis to evaluate controls on historical low-severity fire regimes.Crossref | GoogleScholarGoogle Scholar |
Kennedy MC, McKenzie D (2017) Model evaluation identifies uncertainties and trade-offs in complexity when fire is integrated with hydro-ecological projections. In ‘Natural Hazard Uncertainty Assessment: Modelling and Decision Support’. (Eds K Riley, P Webley, M Thompson) American Geophysical Union Monograph Series, pp. 231–244. (American Geophysical Union: Washington, DC, USA)
Krause A, Kloster S, Wilkenskjeld S, Paeth H (2014) The sensitivity of global wildfires to simulated past, present, and future lightning frequency. Journal of Geophysical Research. Biogeosciences 119, 312–322.
| The sensitivity of global wildfires to simulated past, present, and future lightning frequency.Crossref | GoogleScholarGoogle Scholar |
LANDFIRE (2014) US Department of Interior, geological survey. Available at http://landfire.cr.usgs.gov/viewer/ [Verified 2 March 2017].
Littell JS, Oneil EE, McKenzie D, Hicke JA, Lutz JA, Norheim RA, Elsner MM (2010) Forest ecosystems, disturbance, and climatic change in Washington State, USA. Climatic Change 102, 129–158.
| Forest ecosystems, disturbance, and climatic change in Washington State, USA.Crossref | GoogleScholarGoogle Scholar |
López-Moreno JI, Zabalza J, Vicente-Serrano SM, Revuelto J, Gilaberte M, Azorin-Molina C, Moran-Tejeda E, Garcia-Ruiz JM, Tague C (2014) Impact of climate and land-use change on water availability and reservoir management : scenarios in the Upper Aragón River, Spanish Pyrenees. The Science of the Total Environment 493, 1222–1231.
| Impact of climate and land-use change on water availability and reservoir management : scenarios in the Upper Aragón River, Spanish Pyrenees.Crossref | GoogleScholarGoogle Scholar |
Malamud BD, Millington JDA, Perry GLW (2005) Characterizing wildfire regimes in the United States. Proceedings of the National Academy of Sciences of the United States of America 102, 4694–4699.
| Characterizing wildfire regimes in the United States.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2MXjt1Oiu74%3D&md5=6f24f257653d28586a772fb27b02417aCAS |
Margolis EQ, Balmat J (2009) Fire history and fire–climate relationships along a fire regime gradient in the Santa Fe Municipal Watershed, NM, USA. Forest Ecology and Management 258, 2416–2430.
| Fire history and fire–climate relationships along a fire regime gradient in the Santa Fe Municipal Watershed, NM, USA.Crossref | GoogleScholarGoogle Scholar |
McKenzie D, Kennedy MC (2012) Power laws reveal phase transitions in landscape controls of fire regimes. Nature Communications 3, 726
| Power laws reveal phase transitions in landscape controls of fire regimes.Crossref | GoogleScholarGoogle Scholar |
McKenzie D, Perera A (2015) Modeling wildfire regimes in forest landscapes: abstracting a complex reality. In ‘Simulation Modeling of Forest Landscape Disturbances‘. (Eds A Perera, B Sturdevant, L Buse) pp. 73–92. (Springer: New York, NY, USA)
O’Neill R, Gardner R, Mankin J (1980) Analysis of parameter error in a non-linear model. Ecological Modelling 8, 297–311.
| Analysis of parameter error in a non-linear model.Crossref | GoogleScholarGoogle Scholar |
Reynolds JH, Ford ED (1999) Multicriteria assessment of ecological process models. Ecology 80, 538–553.
| Multicriteria assessment of ecological process models.Crossref | GoogleScholarGoogle Scholar |
Rocca ME, Brown PM, MacDonald LH, Carrico CM (2014) Climate change impacts on fire regimes and key ecosystem services in Rocky Mountain forests. Forest Ecology and Management 327, 290–305.
| Climate change impacts on fire regimes and key ecosystem services in Rocky Mountain forests.Crossref | GoogleScholarGoogle Scholar |
Rothermel RC (1972) A mathematical model for predicting fire spread in wildland fuels. USDA Forest Service, Intermountain Forest and Range Experiment Station, Research Paper INT-116. (Odgen, UT, USA)
Scott JH, Burgan RE (2005) Standard fire behavior fuel models: a comprehensive set for use with Rothermel’s surface fire spread model. USDA Forest Service, Rocky Mountain Research Station, General Technical Report RMRS-GTR-153. (Fort Collins, CO, USA)
Shakesby RA, Doerr SH (2006) Wildfire as a hydrological and geomorphological agent. Earth-Science Reviews 74, 269–307.
| Wildfire as a hydrological and geomorphological agent.Crossref | GoogleScholarGoogle Scholar |
Stavros EN, Abatzoglou JT, McKenzie D, Larkin NK (2014) Regional projections of the likelihood of very large wildland fires under a changing climate in the contiguous western United States. Climatic Change 126, 455–468.
| Regional projections of the likelihood of very large wildland fires under a changing climate in the contiguous western United States.Crossref | GoogleScholarGoogle Scholar |
Stephenson N (1998) Actual evapotranspiration and deficit: biologically meaningful correlates of vegetation distribution across spatial scales. Journal of Biogeography 25, 855–870.
| Actual evapotranspiration and deficit: biologically meaningful correlates of vegetation distribution across spatial scales.Crossref | GoogleScholarGoogle Scholar |
Swann AL, Fung IY, Chiang JC (2012) Mid-latitude afforestation shifts general circulation and tropical precipitation. Proceedings of the National Academy of Sciences of the United States of America 109, 712–716.
| Mid-latitude afforestation shifts general circulation and tropical precipitation.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38XhsFKntL8%3D&md5=236a03bb757b2847719d2b941c67dc0eCAS |
Swetnam T, Falk DA, Hessl AE, Farris C (2011) Reconstructing landscape pattern of historical fires and fire regimes. In ‘Landscape Ecology of Fire. Ecological Studies Vol. 213, Analysis and Synthesis’. (Eds D McKenzie, C Miller, D Falk) Ch. 7, pp. 165–192. (Springer: New York, NY, USA).
Tague C, Band L (2004) RHESSys: regional hydro-ecologic simulation system – an object-oriented approach to spatially distributed modeling of carbon, water, and nutrient cycling. Earth Interactions 8, 1–42.
| RHESSys: regional hydro-ecologic simulation system – an object-oriented approach to spatially distributed modeling of carbon, water, and nutrient cycling.Crossref | GoogleScholarGoogle Scholar |
Tague C, Dugger AL (2010) Ecohydrology and climate change in the mountains of the western USA – a review of research and opportunities. Geography Compass 4, 1648–1663.
| Ecohydrology and climate change in the mountains of the western USA – a review of research and opportunities.Crossref | GoogleScholarGoogle Scholar |
Tague CL, Choate JS, Grant G (2013a) Parameterizing subsurface drainage with geology to improve modeling streamflow responses to climate in data-limited environments. Hydrology and Earth System Sciences 17, 341–354.
| Parameterizing subsurface drainage with geology to improve modeling streamflow responses to climate in data-limited environments.Crossref | GoogleScholarGoogle Scholar |
Tague CL, McDowell NG, Allen CD (2013b) An integrated model of environmental effects on growth, carbohydrate balance, and mortality of Pinus ponderosa forests in the southern Rocky Mountains. PLoS One 8, e80286
| An integrated model of environmental effects on growth, carbohydrate balance, and mortality of Pinus ponderosa forests in the southern Rocky Mountains.Crossref | GoogleScholarGoogle Scholar |
Teensma PDA (1987) Fire history and fire regimes of the Central Western Cascades of Oregon. PhD thesis, Oregon State University, Corvallis, OR, USA.
Weisberg PJ (1998) Fire history, fire regimes, and development of forest structure in the Central Western Oregon Cascades. PhD thesis, Oregon State University, Corvallis, OR, USA.
Weisberg PJ, Ko D, Py C, Bauer JM (2008) Modeling fire and landform influences on the distribution of old-growth pinyon–juniper woodland. Landscape Ecology 23, 931–943.
| Modeling fire and landform influences on the distribution of old-growth pinyon–juniper woodland.Crossref | GoogleScholarGoogle Scholar |
Wood PJ, Hannah DM, Sadler JP (2007) Ecohydrology and hydroecology: an introduction. In ‘Hydroecology and Ecohydrology: Past, Present and Future’. (Eds P Wood, DM Hannah, JP Sadler) pp. 1–6. (Wiley: Chichester, UK)
Zierl B, Bugmann H, Tague CL (2007) Water and carbon fluxes of European ecosystems: an evaluation of the ecohydrological model RHESSys. Hydrological Processes 21, 3328–3339.
| Water and carbon fluxes of European ecosystems: an evaluation of the ecohydrological model RHESSys.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXnvFWltQ%3D%3D&md5=05e71ea63e37c7c06ee524c1f3f03047CAS |